A traditional semantic network is a formal structure for embodying or representing knowledge. It is designed to impose a rigorous structure upon knowledge so that an artificial intelligence computer program or expert system can operate on and reason from and with the knowledge. It is also used to embody the new knowledge resulting from the reasoning of such systems.
Traditional semantic networks constrain how knowledge can be captured and organized so that it fits with the reasoning system's needs for reasoning, while still maintaining enough flexibility to handle variations in kinds of expert knowledge that needs to be reasoned with and about.
Most prior uses of semantic networks in computer software applications have been focused on managing information for the use of expert systems or other artificially intelligent programs. Experts setting about the task of entering their knowledge into such a traditional system are required to learn constrained methods for expressing what they know in a form that the expert system can use and reason with. Consequently, the methods used to operate on these traditional semantic networks have been tailored for the needs of the systems using this knowledge, and consequently lack flexibility.